Deep learning has achieved strong predictive performance in facial emotion recognition (FER), but interpreting the internal representations underlying model decisions remains challenging. In this study, we propose a cross-representation concept-based explainability framework that extends TCAV analysis beyond a single v...
Simona Moldovanu, Sanda Florentina Mihalache, Cătălin Anghel et al.· Applied Informatics· 0 citations
Background: Large language models are increasingly used for rubric-based grading, but it remains unclear whether they adapt selectively when criterion weights change while the response, criterion definitions, and total score remain fixed. This study examined whether alternative point allocations produce targeted criter...
Cătălin Anghel, A. Anghel, Adina Cocu et al.· Informatics· 0 citations
A physician-facing Clinical AI-Readiness Guide for preparing medical datasets before AI-based analysis to improve collaboration between clinical and technical teams and reduce preventable dataset-related failures in medical AI research is proposed.
Cătălin Anghel, A. Anghel, M. Craciun et al.· Journal of Clinical Medicine· 0 citations
Student-history metadata can influence LLM-generated grading scores despite explicit instructions to ignore it, and future LLM-based grading systems should separate answer-based scoring from learner-context-based personalization and validate score invariance under controlled learner-context variations.
Cătălin Anghel, A. Anghel, M. Craciun et al.· Machine Learning and Knowled...· 0 citations
SafetyJudge-LLM shows that local open-weight LLMs can support semantic safety judging, but their reliability must be evaluated across multiple dimensions.
Cătălin Anghel, A. Anghel, M. Craciun et al.· Applied Informatics· 0 citations
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